Overview

Dataset statistics

Number of variables15
Number of observations1197
Missing cells506
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.4 KiB
Average record size in memory120.1 B

Variable types

Categorical5
Numeric10

Alerts

date has a high cardinality: 59 distinct values High cardinality
smv is highly correlated with department and 5 other fieldsHigh correlation
over_time is highly correlated with date and 3 other fieldsHigh correlation
incentive is highly correlated with dateHigh correlation
idle_time is highly correlated with idle_menHigh correlation
idle_men is highly correlated with idle_timeHigh correlation
no_of_workers is highly correlated with department and 3 other fieldsHigh correlation
day is highly correlated with dateHigh correlation
quarter is highly correlated with dateHigh correlation
date is highly correlated with quarter and 6 other fieldsHigh correlation
department is highly correlated with date and 4 other fieldsHigh correlation
team is highly correlated with smv and 1 other fieldsHigh correlation
targeted_productivity is highly correlated with actual_productivityHigh correlation
wip is highly correlated with dateHigh correlation
no_of_style_change is highly correlated with date and 2 other fieldsHigh correlation
actual_productivity is highly correlated with targeted_productivity and 1 other fieldsHigh correlation
wip has 506 (42.3%) missing values Missing
idle_time is highly skewed (γ1 = 20.54542523) Skewed
date is uniformly distributed Uniform
over_time has 31 (2.6%) zeros Zeros
incentive has 604 (50.5%) zeros Zeros
idle_time has 1179 (98.5%) zeros Zeros
idle_men has 1179 (98.5%) zeros Zeros

Reproduction

Analysis started2022-10-19 23:33:20.138937
Analysis finished2022-10-19 23:33:45.172630
Duration25.03 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct59
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
3/11/2015
 
24
1/31/2015
 
24
1/11/2015
 
23
3/10/2015
 
23
1/12/2015
 
23
Other values (54)
1080 

Length

Max length9
Median length9
Mean length8.613199666
Min length8

Characters and Unicode

Total characters10310
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/1/2015
2nd row1/1/2015
3rd row1/1/2015
4th row1/1/2015
5th row1/1/2015

Common Values

ValueCountFrequency (%)
3/11/201524
 
2.0%
1/31/201524
 
2.0%
1/11/201523
 
1.9%
3/10/201523
 
1.9%
1/12/201523
 
1.9%
1/24/201523
 
1.9%
1/8/201522
 
1.8%
1/10/201522
 
1.8%
1/7/201522
 
1.8%
1/13/201522
 
1.8%
Other values (49)969
81.0%

Length

2022-10-19T16:33:45.289474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3/11/201524
 
2.0%
1/31/201524
 
2.0%
1/11/201523
 
1.9%
3/10/201523
 
1.9%
1/12/201523
 
1.9%
1/24/201523
 
1.9%
3/9/201522
 
1.8%
1/22/201522
 
1.8%
3/3/201522
 
1.8%
3/8/201522
 
1.8%
Other values (49)969
81.0%

Most occurring characters

ValueCountFrequency (%)
/2394
23.2%
12314
22.4%
22065
20.0%
51336
13.0%
01276
12.4%
3339
 
3.3%
8145
 
1.4%
4141
 
1.4%
7122
 
1.2%
999
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7916
76.8%
Other Punctuation2394
 
23.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12314
29.2%
22065
26.1%
51336
16.9%
01276
16.1%
3339
 
4.3%
8145
 
1.8%
4141
 
1.8%
7122
 
1.5%
999
 
1.3%
679
 
1.0%
Other Punctuation
ValueCountFrequency (%)
/2394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/2394
23.2%
12314
22.4%
22065
20.0%
51336
13.0%
01276
12.4%
3339
 
3.3%
8145
 
1.4%
4141
 
1.4%
7122
 
1.2%
999
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/2394
23.2%
12314
22.4%
22065
20.0%
51336
13.0%
01276
12.4%
3339
 
3.3%
8145
 
1.4%
4141
 
1.4%
7122
 
1.2%
999
 
1.0%

quarter
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Quarter1
360 
Quarter2
335 
Quarter4
248 
Quarter3
210 
Quarter5
44 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters9576
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQuarter1
2nd rowQuarter1
3rd rowQuarter1
4th rowQuarter1
5th rowQuarter1

Common Values

ValueCountFrequency (%)
Quarter1360
30.1%
Quarter2335
28.0%
Quarter4248
20.7%
Quarter3210
17.5%
Quarter544
 
3.7%

Length

2022-10-19T16:33:45.451480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-19T16:33:45.641418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
quarter1360
30.1%
quarter2335
28.0%
quarter4248
20.7%
quarter3210
17.5%
quarter544
 
3.7%

Most occurring characters

ValueCountFrequency (%)
r2394
25.0%
Q1197
12.5%
u1197
12.5%
a1197
12.5%
t1197
12.5%
e1197
12.5%
1360
 
3.8%
2335
 
3.5%
4248
 
2.6%
3210
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7182
75.0%
Uppercase Letter1197
 
12.5%
Decimal Number1197
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2394
33.3%
u1197
16.7%
a1197
16.7%
t1197
16.7%
e1197
16.7%
Decimal Number
ValueCountFrequency (%)
1360
30.1%
2335
28.0%
4248
20.7%
3210
17.5%
544
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
Q1197
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8379
87.5%
Common1197
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2394
28.6%
Q1197
14.3%
u1197
14.3%
a1197
14.3%
t1197
14.3%
e1197
14.3%
Common
ValueCountFrequency (%)
1360
30.1%
2335
28.0%
4248
20.7%
3210
17.5%
544
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII9576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2394
25.0%
Q1197
12.5%
u1197
12.5%
a1197
12.5%
t1197
12.5%
e1197
12.5%
1360
 
3.8%
2335
 
3.5%
4248
 
2.6%
3210
 
2.2%

department
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
sweing
691 
finishing
257 
finishing
249 

Length

Max length10
Median length6
Mean length7.482873851
Min length6

Characters and Unicode

Total characters8957
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsweing
2nd rowfinishing
3rd rowsweing
4th rowsweing
5th rowsweing

Common Values

ValueCountFrequency (%)
sweing691
57.7%
finishing 257
 
21.5%
finishing249
 
20.8%

Length

2022-10-19T16:33:45.831757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-19T16:33:46.007624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
sweing691
57.7%
finishing506
42.3%

Most occurring characters

ValueCountFrequency (%)
i2209
24.7%
n1703
19.0%
s1197
13.4%
g1197
13.4%
w691
 
7.7%
e691
 
7.7%
f506
 
5.6%
h506
 
5.6%
257
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8700
97.1%
Space Separator257
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i2209
25.4%
n1703
19.6%
s1197
13.8%
g1197
13.8%
w691
 
7.9%
e691
 
7.9%
f506
 
5.8%
h506
 
5.8%
Space Separator
ValueCountFrequency (%)
257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8700
97.1%
Common257
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i2209
25.4%
n1703
19.6%
s1197
13.8%
g1197
13.8%
w691
 
7.9%
e691
 
7.9%
f506
 
5.8%
h506
 
5.8%
Common
ValueCountFrequency (%)
257
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i2209
24.7%
n1703
19.0%
s1197
13.4%
g1197
13.4%
w691
 
7.7%
e691
 
7.7%
f506
 
5.6%
h506
 
5.6%
257
 
2.9%

day
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Wednesday
208 
Sunday
203 
Tuesday
201 
Thursday
199 
Monday
199 

Length

Max length9
Median length8
Mean length7.334168755
Min length6

Characters and Unicode

Total characters8779
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Wednesday208
17.4%
Sunday203
17.0%
Tuesday201
16.8%
Thursday199
16.6%
Monday199
16.6%
Saturday187
15.6%

Length

2022-10-19T16:33:46.160372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-19T16:33:46.307661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
wednesday208
17.4%
sunday203
17.0%
tuesday201
16.8%
thursday199
16.6%
monday199
16.6%
saturday187
15.6%

Most occurring characters

ValueCountFrequency (%)
d1405
16.0%
a1384
15.8%
y1197
13.6%
u790
9.0%
e617
7.0%
n610
6.9%
s608
6.9%
T400
 
4.6%
S390
 
4.4%
r386
 
4.4%
Other values (5)992
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7582
86.4%
Uppercase Letter1197
 
13.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d1405
18.5%
a1384
18.3%
y1197
15.8%
u790
10.4%
e617
8.1%
n610
8.0%
s608
8.0%
r386
 
5.1%
h199
 
2.6%
o199
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
T400
33.4%
S390
32.6%
W208
17.4%
M199
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin8779
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d1405
16.0%
a1384
15.8%
y1197
13.6%
u790
9.0%
e617
7.0%
n610
6.9%
s608
6.9%
T400
 
4.6%
S390
 
4.4%
r386
 
4.4%
Other values (5)992
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d1405
16.0%
a1384
15.8%
y1197
13.6%
u790
9.0%
e617
7.0%
n610
6.9%
s608
6.9%
T400
 
4.6%
S390
 
4.4%
r386
 
4.4%
Other values (5)992
11.3%

team
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.426900585
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:46.446695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.463963285
Coefficient of variation (CV)0.5389788187
Kurtosis-1.223905739
Mean6.426900585
Median Absolute Deviation (MAD)3
Skewness0.009847502797
Sum7693
Variance11.99904164
MonotonicityNot monotonic
2022-10-19T16:33:46.593810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8109
9.1%
2109
9.1%
1105
8.8%
4105
8.8%
9104
8.7%
10100
8.4%
1299
8.3%
796
8.0%
395
7.9%
694
7.9%
Other values (2)181
15.1%
ValueCountFrequency (%)
1105
8.8%
2109
9.1%
395
7.9%
4105
8.8%
593
7.8%
694
7.9%
796
8.0%
8109
9.1%
9104
8.7%
10100
8.4%
ValueCountFrequency (%)
1299
8.3%
1188
7.4%
10100
8.4%
9104
8.7%
8109
9.1%
796
8.0%
694
7.9%
593
7.8%
4105
8.8%
395
7.9%

targeted_productivity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7296324144
Minimum0.07
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:46.746331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.5
Q10.7
median0.75
Q30.8
95-th percentile0.8
Maximum0.8
Range0.73
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.09789096326
Coefficient of variation (CV)0.1341647675
Kurtosis5.613700571
Mean0.7296324144
Median Absolute Deviation (MAD)0.05
Skewness-2.144150033
Sum873.37
Variance0.009582640688
MonotonicityNot monotonic
2022-10-19T16:33:46.894804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.8540
45.1%
0.7242
20.2%
0.75216
 
18.0%
0.6563
 
5.3%
0.657
 
4.8%
0.549
 
4.1%
0.3527
 
2.3%
0.42
 
0.2%
0.071
 
0.1%
ValueCountFrequency (%)
0.071
 
0.1%
0.3527
 
2.3%
0.42
 
0.2%
0.549
 
4.1%
0.657
 
4.8%
0.6563
 
5.3%
0.7242
20.2%
0.75216
 
18.0%
0.8540
45.1%
ValueCountFrequency (%)
0.8540
45.1%
0.75216
 
18.0%
0.7242
20.2%
0.6563
 
5.3%
0.657
 
4.8%
0.549
 
4.1%
0.42
 
0.2%
0.3527
 
2.3%
0.071
 
0.1%

smv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0621721
Minimum2.9
Maximum54.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:47.073962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile2.9
Q13.94
median15.26
Q324.26
95-th percentile30.1
Maximum54.56
Range51.66
Interquartile range (IQR)20.32

Descriptive statistics

Standard deviation10.9432192
Coefficient of variation (CV)0.7265365931
Kurtosis-0.7953459064
Mean15.0621721
Median Absolute Deviation (MAD)11.11
Skewness0.4059367369
Sum18029.42
Variance119.7540464
MonotonicityNot monotonic
2022-10-19T16:33:47.227167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.94192
16.0%
2.9108
 
9.0%
22.52103
 
8.6%
30.179
 
6.6%
4.1576
 
6.3%
18.7950
 
4.2%
4.646
 
3.8%
15.2644
 
3.7%
25.932
 
2.7%
11.6131
 
2.6%
Other values (60)436
36.4%
ValueCountFrequency (%)
2.9108
9.0%
3.920
 
1.7%
3.94192
16.0%
4.0821
 
1.8%
4.1576
 
6.3%
4.317
 
1.4%
4.646
 
3.8%
5.1326
 
2.2%
10.056
 
0.5%
11.4130
 
2.5%
ValueCountFrequency (%)
54.561
0.1%
51.021
0.1%
50.891
0.1%
50.482
0.2%
49.11
0.1%
48.842
0.2%
48.681
0.1%
48.181
0.1%
45.671
0.1%
42.972
0.2%

wip
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct548
Distinct (%)79.3%
Missing506
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean1190.465991
Minimum7
Maximum23122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:47.383586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile358.5
Q1774.5
median1039
Q31252.5
95-th percentile1602
Maximum23122
Range23115
Interquartile range (IQR)478

Descriptive statistics

Standard deviation1837.455001
Coefficient of variation (CV)1.543475424
Kurtosis101.7020449
Mean1190.465991
Median Absolute Deviation (MAD)232
Skewness9.741786274
Sum822612
Variance3376240.881
MonotonicityNot monotonic
2022-10-19T16:33:47.553714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10395
 
0.4%
12824
 
0.3%
14223
 
0.3%
12163
 
0.3%
14133
 
0.3%
14483
 
0.3%
7593
 
0.3%
10953
 
0.3%
11083
 
0.3%
10793
 
0.3%
Other values (538)658
55.0%
(Missing)506
42.3%
ValueCountFrequency (%)
71
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
141
0.1%
151
0.1%
291
0.1%
301
0.1%
521
0.1%
ValueCountFrequency (%)
231221
0.1%
215401
0.1%
213851
0.1%
212661
0.1%
168821
0.1%
122611
0.1%
97921
0.1%
89921
0.1%
29841
0.1%
26981
0.1%

over_time
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct143
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4567.460317
Minimum0
Maximum25920
Zeros31
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:48.100045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile960
Q11440
median3960
Q36960
95-th percentile10368
Maximum25920
Range25920
Interquartile range (IQR)5520

Descriptive statistics

Standard deviation3348.823563
Coefficient of variation (CV)0.7331916054
Kurtosis0.4243642959
Mean4567.460317
Median Absolute Deviation (MAD)2760
Skewness0.6732872953
Sum5467250
Variance11214619.26
MonotonicityNot monotonic
2022-10-19T16:33:48.273911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
960129
 
10.8%
1440111
 
9.3%
696061
 
5.1%
684048
 
4.0%
120039
 
3.3%
180038
 
3.2%
1017036
 
3.0%
031
 
2.6%
336030
 
2.5%
408030
 
2.5%
Other values (133)644
53.8%
ValueCountFrequency (%)
031
 
2.6%
1201
 
0.1%
2406
 
0.5%
3602
 
0.2%
4801
 
0.1%
6004
 
0.3%
7204
 
0.3%
8402
 
0.2%
9002
 
0.2%
960129
10.8%
ValueCountFrequency (%)
259201
 
0.1%
151201
 
0.1%
150002
 
0.2%
146401
 
0.1%
138001
 
0.1%
126001
 
0.1%
121801
 
0.1%
120001
 
0.1%
107701
 
0.1%
1062022
1.8%

incentive
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct48
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.21052632
Minimum0
Maximum3600
Zeros604
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:48.468854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350
95-th percentile88
Maximum3600
Range3600
Interquartile range (IQR)50

Descriptive statistics

Standard deviation160.1826428
Coefficient of variation (CV)4.192107732
Kurtosis299.0324621
Mean38.21052632
Median Absolute Deviation (MAD)0
Skewness15.79074602
Sum45738
Variance25658.47905
MonotonicityNot monotonic
2022-10-19T16:33:48.688208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0604
50.5%
50113
 
9.4%
6361
 
5.1%
4554
 
4.5%
3052
 
4.3%
2338
 
3.2%
3829
 
2.4%
6028
 
2.3%
4027
 
2.3%
7524
 
2.0%
Other values (38)167
 
14.0%
ValueCountFrequency (%)
0604
50.5%
211
 
0.1%
2338
 
3.2%
242
 
0.2%
251
 
0.1%
269
 
0.8%
272
 
0.2%
291
 
0.1%
3052
 
4.3%
321
 
0.1%
ValueCountFrequency (%)
36001
 
0.1%
28801
 
0.1%
14401
 
0.1%
12001
 
0.1%
10801
 
0.1%
9605
 
0.4%
1381
 
0.1%
1192
 
0.2%
11321
1.8%
1007
 
0.6%

idle_time
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7301587302
Minimum0
Maximum300
Zeros1179
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:48.888186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum300
Range300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.70975652
Coefficient of variation (CV)17.40684045
Kurtosis442.6381603
Mean0.7301587302
Median Absolute Deviation (MAD)0
Skewness20.54542523
Sum874
Variance161.5379108
MonotonicityNot monotonic
2022-10-19T16:33:49.053912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
01179
98.5%
3.53
 
0.3%
22
 
0.2%
52
 
0.2%
82
 
0.2%
4.52
 
0.2%
42
 
0.2%
901
 
0.1%
1501
 
0.1%
2701
 
0.1%
Other values (2)2
 
0.2%
ValueCountFrequency (%)
01179
98.5%
22
 
0.2%
3.53
 
0.3%
42
 
0.2%
4.52
 
0.2%
52
 
0.2%
6.51
 
0.1%
82
 
0.2%
901
 
0.1%
1501
 
0.1%
ValueCountFrequency (%)
3001
 
0.1%
2701
 
0.1%
1501
 
0.1%
901
 
0.1%
82
0.2%
6.51
 
0.1%
52
0.2%
4.52
0.2%
42
0.2%
3.53
0.3%

idle_men
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3692564745
Minimum0
Maximum45
Zeros1179
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:49.268815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum45
Range45
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.268987324
Coefficient of variation (CV)8.852891011
Kurtosis102.9628693
Mean0.3692564745
Median Absolute Deviation (MAD)0
Skewness9.855079124
Sum442
Variance10.68627813
MonotonicityNot monotonic
2022-10-19T16:33:49.387225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
01179
98.5%
103
 
0.3%
153
 
0.3%
303
 
0.3%
203
 
0.3%
352
 
0.2%
451
 
0.1%
371
 
0.1%
251
 
0.1%
401
 
0.1%
ValueCountFrequency (%)
01179
98.5%
103
 
0.3%
153
 
0.3%
203
 
0.3%
251
 
0.1%
303
 
0.3%
352
 
0.2%
371
 
0.1%
401
 
0.1%
451
 
0.1%
ValueCountFrequency (%)
451
 
0.1%
401
 
0.1%
371
 
0.1%
352
 
0.2%
303
 
0.3%
251
 
0.1%
203
 
0.3%
153
 
0.3%
103
 
0.3%
01179
98.5%

no_of_style_change
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
0
1050 
1
114 
2
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1197
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Length

2022-10-19T16:33:49.503073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-19T16:33:49.651862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Most occurring characters

ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1197
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common1197
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01050
87.7%
1114
 
9.5%
233
 
2.8%

no_of_workers
Real number (ℝ≥0)

HIGH CORRELATION

Distinct61
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.60985798
Minimum2
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:49.858307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median34
Q357
95-th percentile59
Maximum89
Range87
Interquartile range (IQR)48

Descriptive statistics

Standard deviation22.19768668
Coefficient of variation (CV)0.6413689041
Kurtosis-1.788107905
Mean34.60985798
Median Absolute Deviation (MAD)24
Skewness-0.1117397312
Sum41428
Variance492.737294
MonotonicityNot monotonic
2022-10-19T16:33:50.082789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8262
21.9%
58114
 
9.5%
57109
 
9.1%
5975
 
6.3%
1060
 
5.0%
56.554
 
4.5%
5649
 
4.1%
3443
 
3.6%
942
 
3.5%
1237
 
3.1%
Other values (51)352
29.4%
ValueCountFrequency (%)
26
 
0.5%
41
 
0.1%
53
 
0.3%
61
 
0.1%
73
 
0.3%
8262
21.9%
942
 
3.5%
1060
 
5.0%
111
 
0.1%
1237
 
3.1%
ValueCountFrequency (%)
891
 
0.1%
607
 
0.6%
59.55
 
0.4%
5975
6.3%
58.521
 
1.8%
58114
9.5%
57.525
 
2.1%
57109
9.1%
56.554
4.5%
5649
4.1%

actual_productivity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct879
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.735091097
Minimum0.233705476
Maximum1.1204375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2022-10-19T16:33:50.302000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.233705476
5-th percentile0.3555131924
Q10.650307143
median0.773333333
Q30.850252525
95-th percentile0.977037879
Maximum1.1204375
Range0.886732024
Interquartile range (IQR)0.199945382

Descriptive statistics

Standard deviation0.1744879035
Coefficient of variation (CV)0.2373690883
Kurtosis0.3332273412
Mean0.735091097
Median Absolute Deviation (MAD)0.090833333
Skewness-0.8074917745
Sum879.9040431
Variance0.03044602847
MonotonicityNot monotonic
2022-10-19T16:33:50.520880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.80040196124
 
2.0%
0.97186666712
 
1.0%
0.85013676612
 
1.0%
0.7506510111
 
0.9%
0.85050231111
 
0.9%
1.00023040911
 
0.9%
0.8001287218
 
0.7%
0.7503955138
 
0.7%
0.8581439397
 
0.6%
0.8001171037
 
0.6%
Other values (869)1086
90.7%
ValueCountFrequency (%)
0.2337054761
0.1%
0.2357954551
0.1%
0.2380416671
0.1%
0.246251
0.1%
0.2473160171
0.1%
0.2494166671
0.1%
0.2513992541
0.1%
0.25651
0.1%
0.2581
0.1%
0.2593751
0.1%
ValueCountFrequency (%)
1.12043751
0.1%
1.1081251
0.1%
1.1004839181
0.1%
1.0966333331
0.1%
1.0596212121
0.1%
1.0579629631
0.1%
1.0506666671
0.1%
1.050280581
0.1%
1.0335700761
0.1%
1.0331555561
0.1%

Interactions

2022-10-19T16:33:42.462578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:24.997298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:27.761675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:29.621963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:31.467149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:33.348588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:35.215548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:37.170280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:38.846881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:40.867530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:42.657533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:25.205998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:27.935874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:29.767318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:31.655092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:33.539886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:35.462035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:37.356563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:38.983201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:41.012198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:42.833032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:25.389134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:28.169210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:29.946916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:31.847233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:33.753350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:35.705334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:37.544510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:39.169775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:41.166963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:43.015916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:25.537747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:28.371315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:30.146923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:31.977765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:33.942983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:35.907332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:37.709606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:39.350312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:41.298483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:43.189670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:25.714916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:28.538956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:30.365289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:32.117157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:34.102846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:36.083119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:37.830213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:39.576755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:41.472884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:43.344101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:25.911514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:28.690080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:30.601669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:32.286490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:34.267495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:36.280323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:37.997870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:39.751424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:41.639034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:43.515704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:26.114436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:28.836631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:30.837503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:32.459332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:34.431032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:36.450686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:38.159532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:40.116815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:41.818822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:43.698057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:27.312744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:29.030330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:31.000366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:32.639565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:34.619446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:36.653188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:38.330890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:40.284401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:42.003580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:43.882563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:27.484735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:29.221011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:31.148377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:33.048397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:34.809955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:36.813242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:38.484979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:40.472319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:42.136151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:44.082456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:27.603809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:29.468013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:31.294814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:33.196769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:34.993533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:36.966451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:38.665212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:40.681270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:33:42.275956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-19T16:33:50.692481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-19T16:33:50.987143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-19T16:33:51.322560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-19T16:33:51.638995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-19T16:33:51.829757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-19T16:33:44.371523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-19T16:33:44.781257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-19T16:33:44.986840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

datequarterdepartmentdayteamtargeted_productivitysmvwipover_timeincentiveidle_timeidle_menno_of_style_changeno_of_workersactual_productivity
01/1/2015Quarter1sweingThursday80.8026.161108.07080980.00059.00.940725
11/1/2015Quarter1finishingThursday10.753.94NaN96000.0008.00.886500
21/1/2015Quarter1sweingThursday110.8011.41968.03660500.00030.50.800570
31/1/2015Quarter1sweingThursday120.8011.41968.03660500.00030.50.800570
41/1/2015Quarter1sweingThursday60.8025.901170.01920500.00056.00.800382
51/1/2015Quarter1sweingThursday70.8025.90984.06720380.00056.00.800125
61/1/2015Quarter1finishingThursday20.753.94NaN96000.0008.00.755167
71/1/2015Quarter1sweingThursday30.7528.08795.06900450.00057.50.753683
81/1/2015Quarter1sweingThursday20.7519.87733.06000340.00055.00.753098
91/1/2015Quarter1sweingThursday10.7528.08681.06900450.00057.50.750428

Last rows

datequarterdepartmentdayteamtargeted_productivitysmvwipover_timeincentiveidle_timeidle_menno_of_style_changeno_of_workersactual_productivity
11873/11/2015Quarter2sweingWednesday40.7526.821054.07080450.00059.00.750051
11883/11/2015Quarter2sweingWednesday50.7026.82992.06960300.00158.00.700557
11893/11/2015Quarter2sweingWednesday80.7030.48914.06840300.00157.00.700505
11903/11/2015Quarter2sweingWednesday60.7023.411128.04560400.00138.00.700246
11913/11/2015Quarter2sweingWednesday70.6530.48935.06840260.00157.00.650596
11923/11/2015Quarter2finishingWednesday100.752.90NaN96000.0008.00.628333
11933/11/2015Quarter2finishingWednesday80.703.90NaN96000.0008.00.625625
11943/11/2015Quarter2finishingWednesday70.653.90NaN96000.0008.00.625625
11953/11/2015Quarter2finishingWednesday90.752.90NaN180000.00015.00.505889
11963/11/2015Quarter2finishingWednesday60.702.90NaN72000.0006.00.394722